Machine Learning Crash Course - <AUTHOR> https://developers.google.com/machine-learning/crash-course/ <comment><USER2> Looking through the topics covered, the standard AI-course caveats ([https://news.ycombinator.com/item?id=16247629](https://news.ycombinator.com/item?id=16247629)) apply.

Yes, AI/ML MOOCs teach the corresponding tools well, and the creation of new tools like Keras make the field much more accessible. The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about the industry.

However, contrary to the thought pieces that tend to pop up, taking and passing a crash course doesn't mean you'll be an expert in the field (and this applies for most MOOCs, honestly). They're very good for learning an overview of the technology, but nothing beats applying the tools on a real-world, noisy dataset, and solving the inevitable little problems that crop up during the process.

Reviewing the Keras documentation ([https://keras.io](https://keras.io)) and examples ([https://github.com/keras- team/keras/tree/master/examples](https://github.com/keras- team/keras/tree/master/examples)) are honestly much better teachers of AI/ML than any MOOC, in my opinion.

(Of course, Keras is now a part of TensorFlow, so there's a neat Google vertical intergration with this crash course!)

<comment><USER3> Unless you work for a company obviously known for their ML the "expertise" out there right now is brutal. People are building recommendation engines without knowing the very, very, very basics like Jaccard indexes, ROC Curves, or topic drift. I've even had to explain type two error to someone working on one of these before.

I agree with your general thrust, and you're right, messy data is often 95% of the problem, but even going through just the Google courses will put people in the top 15% in most cities.

<comment><USER4> I took a machine learning graduate-level course from Andrew Ng himself, and I don't recall learning about Jaccard indexes or topic drift. Maybe your sense of what counts as "very, very, very basic" is skewed toward your own experience. There's a phenomenon known to psychologists where people tend to think that the stuff that they know is very easy and basic, so they conclude that anybody who doesn't know what they know must be uneducated. But then it turns out that the person you think is uneducated knows about a bunch of surprising stuff that you don't. I can't remember the term for this phenomenon, but I often remember it whenever I find myself beginning to judge another person's expertise. This phenomenon is also super relevant to the failings of most technical interviews, in my opinion.

<comment><USER5> There's a bit of snobbiness in different areas of tech, although there also are in different areas of academia and research. At the end of the day, the most successful people are the ones who wouldn't dismiss a DS who didn't know "Jaccard index" or "the Halting Problem".

</comment></comment></comment></comment><comment><USER6> Great to see they have a nice introductory section to feature engineering! Feature engineering is often the most impactful thing you can do to improve quality of models and a place where I often see beginners (and experts for that matter) get stuck. Google walks through how to work with json files and categorical variables [https://developers.google.com/machine-learning/crash- course/...](https://developers.google.com/machine-learning/crash- course/representation/feature-engineering).

If anyone is looking to get more indepth, I work on an open source python library for automated feature engineering called Featuretools [https://github.com/featuretools/featuretools/](https://github.com/featuretools/featuretools/). It can help when your data is more complex such as when it comprised of multiple tables.

We have several demos you can run yourself to apply it to real datasets here: [https://www.featuretools.com/demos](https://www.featuretools.com/demos).

<comment><USER7> Your comment got me interested in this course. However, all I could find about feature engineering there is what you linked to, directly.

Given that entire scientific careers, books, and conferences are built around the topic of feature engineering, and at least IMO good ML tools live or die with good feature engineering (in its broadest sense, for you deep learning fanatics :-)) that doesn't seem like more than the bare minimum I'd expect from _any_ ML "crash-course" that is to be taken serious (and I wouldn't expect an ounce less from Google... :-)).

Am I missing something, maybe?

In any case, nice work of your own, and thanks for sharing it!

<comment><USER6> 10 seconds into the video of feature engineering they say that feature engineering takes up about 75% of the time [https://developers.google.com/machine-learning/crash- course/...](https://developers.google.com/machine-learning/crash- course/representation/video-lecture)

They understand the value, but but if you keep watching, they don't seen go beyond the basic.

</comment></comment></comment><comment><USER8> For those interested in a deeper dive to just deep learning, "Tensorflow and deep learning - without a PhD" is really good, and covers a lot of material in a single 2hr talk.

[https://www.youtube.com/watch?v=vq2nnJ4g6N0](https://www.youtube.com/watch?v=vq2nnJ4g6N0)

<comment><USER9> +1 for this. Well worth the 2 hours.

</comment></comment><comment><USER10> This looks like a well put-together course, and a good way to learn TensorFlow. Keras and TensorFlow are top of my list of technologies to explore in the very near future.

Is anyone here doing Andrew Ng's Machine Learning course [1]? I'm about half- way through and really enjoying it. I'm particularly appreciating that the programming exercises are done in MatLab/Octave, so I feel that I'm really understanding the fundamentals without an API getting in the way, and developing some good intuition. Obviously frameworks are the way to go for production ML work, but I wonder whether ML people here think this bottom-up approach is advisable or could it be misleading when I move on to Keras/TensorFlow/whatever?

[1] [https://www.coursera.org/learn/machine- learning](https://www.coursera.org/learn/machine-learning)

Edit: brevity

<comment> johnx123-up I used to download all ML videos for offline ad hoc watching. But, this crash course videos cannot be downloaded using 'youtube-dl'. Any recommendations?

<comment><USER11> Where are the videos anyway? I don't see any play button or ay console to play the videos.

</comment></comment></comment><comment><USER12> The choice of TensorFlow is a bit disappointing for a beginner-focused course which looks really solid otherwise. Business seems to have gotten priority over pedagogy in that case.

I see TensorFlow as the Angular of machine learning: first on the market, powerful but unwiedly. Like Angular, it will ultimately get superseded by tools with a nicer API (scikit-learn, Keras) or more versatility (PyTorch). Like Angular, it's probably not the best choice for a beginner to invest time into.

<comment><USER13> Add to that that TensorFlow was practically a latecomer, not the first to market.

</comment></comment><comment><USER14> I love the prework section: [https://developers.google.com/machine- learning/crash-course/...](https://developers.google.com/machine- learning/crash-course/prereqs-and-prework) It's a very good mix of topic and skills that I think everyone should learn, even if not directly planning to do ML or DL. If y'all are looking for a compact (and inexpensive) textbook on linear algebra that comes with all prerequisites you can check out: [https://gum.co/noBSLA](https://gum.co/noBSLA) (disclaimer: I wrotes it)

</comment><comment><USER15> Link to the exercises: [https://developers.google.com/machine-learning/crash- course/...](https://developers.google.com/machine-learning/crash- course/exercises)

</comment><comment><USER16> As someone who just did this internally:

Do it. It's worth your time. Very well paced exercises, and it walks you through the flow quite nicely.

<comment><USER17> I went through the first a couple of topics. It seemed very disjointed. Different people presenting, different exercises. Was it like this internally? Or is this heavily "annotated"?

</comment></comment><comment><USER18> I like this move from google. Sure it is targeted for you to use Tensorflow but more courseware and MOOCs help everyone. I love doing self study and Tensorflow's tutorials are top notch. Since I can also use Tensorflow on my own hardware and anywhere else I really love better docs and MOOCs in general. What I really want to do is understand enough Tensorflow to reproduce other people's experiments in their papers on github and I think this would be one of the best ways to do this. Of course, this may eat into a bunch of companies that have paid programs for ML but its Google's prerogative to make ML cheaper and easier to deploy and learn so I am all for that.

</comment><comment><USER19> _we recommend that students meet the following prerequisites:Mastery of intro- level algebra. You should be comfortable with variables and coefficients, linear equations, graphs of functions, and histograms_

Any book suggestions to getting up to speed in this area?

<comment><USER20> Khan Academy probably has this covered.

<comment><USER21> A shame Google doesn't just link to the Khan Academy course.

<comment><USER22> not true. they do have khan academy link where applicable. for example for the algebra ones. check below link [https://developers.google.com/machine- learning/crash-course/...](https://developers.google.com/machine- learning/crash-course/prereqs-and-prework#prerequisites)

</comment></comment></comment></comment><comment><USER23> Shameless plug: Lambda School (YC S17) is also putting on a free Machine Learning crash course (we call it a mini bootcamp), followed by an optional 6-12 month course that you pay for once you get a job in data science (it's free until then, and always free if you don't get a job in ML).

[https://lambdaschool.com/machine-learning- bootcamp/](https://lambdaschool.com/machine-learning-bootcamp/)

<comment><USER24> is there a more fleshed out outline for what will be covered here? sounds interesting

</comment></comment><comment><USER25> As someone who is trying to learn ML, all the courses available are hugely helpful. One thing I wish I had easy access to is the process that someone goes through while trying to build a model on a real dataset.

Specifically following questions are the ones I struggle with:

1\. How did you figure out what features would be useful?

2\. How did you figure out what algorithm(s) are appropriate?

3\. how and why did you massage the data in a specific way?

<comment><USER26> > How did you figure out what features would be useful?

There are various feature engineering and feature extraction techniques. Filter methods, wrapper methods, and embedded methods. Principle component analysis, autoencoding, variance analysis, linear discriminant analysis, Gini index, genetic algorithms, etc -- the feature selection process will depend on the dataset, the problem domain, the analysis algorithm you ultimately use, etc.

> How did you figure out what algorithm(s) are appropriate?

Also depends on the problem domain. Discrete or continuous data? Categorical features, numeric features, features as bitmasks. Do you need a probabilistic outcome? Etc.

Generally you start with the easiest algorithms in your toolbox to see how viable they are. For a classification task I'll almost always start with a naive Bayes classifier (if the data allows) and/or a random forest and see how they perform. If the problem domain is highly non-linear you might start with a support vector or kernel method. Neural network is a last resort for me, as I find most classification problems can be solved to a high accuracy much more simply.

> how and why did you massage the data in a specific way?

This relates back to #1 -- you should only massage data based on what your feature engineering tells you to do. Sometimes you might want to remove outliers or clean up the training data, but only if the outliers really should be removed from consideration entirely.

<comment><USER25> Thanks for the response!

> There are various feature engineering and feature extraction techniques. > Filter methods, wrapper methods, and embedded methods. Principle component > analysis, autoencoding, variance analysis, linear discriminant analysis, > Gini index, genetic algorithms, etc -- the feature selection process will > depend on the dataset, the problem domain, the analysis algorithm you > ultimately use, etc.

Obviously thats a big toolbox and Im sure it takes time to develop an intuitive understanding for all these techniques. What I hope for is some sort guidebook on what to look for when I stumble across problems. So lets say you try out an algorithm and your accuracy(or whatever evaluation criteria you might have) is low. How do you figure out if thats due to the algorithm, or is it due to (or due to the lack of) feature selection?

An analogy that might be useful is, when I see my database queries are slow, I can use EXPLAIN to guide what knobs to tune. Obviously it requires understanding what indexes are, what a full table scan is etc. etc. but the EXPLAIN plan provides a guidebook of sorts.

<comment><USER26> Every problem is different, so the only advice I can give is: research research research! Do the hard work up-front; figure out how to _describe_ your problem in a mathematical sense, and identify the right tools to use for the shape of your input, output and problem dimensions. What's the distribution of each dimension. Are the relationships linear, nonlinear, clustered, dispersed, logarithmic, etc. Once you know those things, you're able to narrow in on the right tools and analyses to use.

</comment></comment></comment></comment><comment><USER27> I've compiled this into a Todoist template you can import - it's got links to each module + times.

To preview: [http://todotemplates.com/posts/HRtYanEq8zMgRL5fz/google- ml-c...](http://todotemplates.com/posts/HRtYanEq8zMgRL5fz/google-ml-crash- course)

To import directly: [https://todoist.com/importFromTemplate?t_url=https%3A%2F%2Fd...](https://todoist.com/importFromTemplate?t_url=https%3A%2F%2Fd1aspxi4rjqbaz.cloudfront.net%2F24fc6edd6dff480670528e9f842cb494_Google%2520ML%2520crash%2520course.csv)

</comment><comment><USER28> Cool tutorial, but I'm not entirely sure what makes this ML -- aside from neural nets, this is more or less the material you'd encounter in a basic applied statistics or regression analysis course, minus material on estimating uncertainty, modeling survival or time-series data, and causal inference. I suspect you'd benefit more from a 50 minute tutorial on those than neural nets.

</comment><comment><USER29> I want to ask people who know ML well if the hype is warranted?

Billions of courses, web sites, job applications and HN posts. The subject seem to have taken off massively in the last two years. I mean image and speech recognition is pretty cool (when it works!), but hardly that earth shattering, is it?

<comment><USER30> Deep nets are deservedly big because they've managed to improve upon _most_ of the decades-old state-of-the-art methods in the world of signal processing (DSP): voice, image, video, game play, and a significant amount of natural language. No other single computational/algorithmic method has achieved so much in so many domains, ever. That's revolutionary.

The rate of advance using deep nets in signal processing will likely slow down now, but they aren't going away, not in the foreseeable future.

The hype around DNNs arose when we took our unbridled enthusiasm for what's they've achieved in DSP and extended it to other domains where data is less 'dense' and thus aren't as amenable to fast de/convolution in N-D space or time.

Will DNNs revolutionize or introduce _all_ the techniques needed to achieve AGI/Strong AI? I very much doubt it. As yet, there's little sign that DNNs can perform relational operations on interdependent symbols, like the transforms available via type theory, bayesian nets, or predicate logic.

The multitude of disparate facts and semantics in a rich knowledgebase can't be organized into dense matrices the way that continuous signals can, so the SIMD operations that are so effective in DSP won't implement the rich transformations needed in a relational fact-based knowledge space equally as well, if at all. Thus DNNs almost surely aren't going to take us to the heights of logical or compositional thinking that human level intelligence requires.

But how far up relational mountain _will_ DNNs take us? I suspect that won't be known for a decade or longer. But even if we don't reach the summit, we'll be considerably closer than we were before.

<comment><USER31> This is a really fantastic and interesting look at ML, for someone who's just beginning first steps. Any recommendations on where else I can read about the areas DNNs and associated stuff (GANs, RL, etc) in terms of what they'll likely not be capable of in the near mid term?

</comment></comment></comment><comment><USER32> I've been itching to learn a bit about the industry and to be able to create & train ML models myself; I'm glad Google decided to put out a course where I wouldn't have to worry about the quality of instruction.

</comment><comment><USER33> On a sidenote, can someone talk about what kind of tools are being used to integrate the subtitles and scrolling behavior over a youtube video in this course? Is there an OS implementation?

</comment><comment><USER34> Thanks Google! Now I know that I am a ML guy, as an economist and econometrician. Yes, we shoot this on all kind of stuff, though with a clear business acumen or economic policy thinking.

</comment><comment><USER35> I have a new project at work: I need to take in a free form text of recipe ingredients (e.g. "1/2 cup diced onions", "two potatoes, cut into 1-inch cubes", etc.) and build a program that identifies the ingredient (e.g. onion, potato), as well as the quantity (e.g. 0.5 cup, 2.0 units). Would machine learning be an applicable approach to solving this? Right now I'm just planning on using an NLP library to parse out the various parts of the ingredient text.

<comment><USER36> It's not very sexy, but I think you might find it easier and more robust just to use an NLP library.

I built something similar (albeit for a relatively limited database of recipes) for a hackathon a couple of weeks back. I didn't even use a proper NLP library, just some simple hand-rolled pattern-matching, and got pretty good results.

Good luck!

<comment><USER35> I think you're right. Did you happen to open-source your code from the hackathon? I'd love to take a look at your approach if you don't mind.

<comment><USER36> Sorry, I normally would but one of the other team members is considering taking the hack forward and wanted to keep it closed for now. (It's hard to see how much competitive advantage he'd have from 48 hours of very-hacked- together code, but so few hackathon projects get taken forward that I didn't want to discourage him!)

The approach was to tokenize the input and then do basic pattern-matching on it, with separate dictionaries of quantity units (e.g. cup, oz, pound) ingredients, processing words (e.g. "chopped") and throw-away words (e.g. "of"). In fact, possibly the most complicated part was parsing "2.5", "2 and a half" and "2½" all to the same thing.

</comment></comment></comment></comment><comment><USER37> The ulterior motive behind this is to increase usage of Google Cloud (according to this answer on Quora: [https://www.quora.com/Why-did-Google- release-their-machine-l...](https://www.quora.com/Why-did-Google-release- their-machine-learning-crash-course/answer/Carlos-Matias-La- Borde?share=00711d71&srid=XvlZ))

</comment><comment><USER38> Cool! Does anybody also know a good blockchain crash course of similar kind so one could grok all the major buzzwords of today?

<comment><USER39> I loved this one a lot: [https://anders.com/blockchain/](https://anders.com/blockchain/)

<comment><USER11> This is what got me a proper intuition for why a blockchain can be useful.

</comment></comment></comment><comment><USER40> Unless you have already invested a lot of time into learning (and building on top of) TF, I would advise to pick up PyTorch. It's much easier to learn and use (imperative!), and has higher performance on common workloads.

<comment><USER41> Except there aren't many good resources to learn it and the documentation isn't very good. Hopefully this will improve soon.

<comment><USER40> On the positive side with PyTorch you don't need nearly as much documentation as you would with TF. TF in general feels like it's fighting you every step of the way. There's a lot of cognitive overhead. Not so with PyTorch. Everything is straightforward, and can be run/examined in ipython.

</comment></comment></comment><comment><USER42> While I like the idea, in principle, that you don't need a CS education to use AI/ML, I doubt it. Here's a problem that cropped up today: Our instance ran out of hard drive space on a training set of ~400,000 images. The individual images were only 375 GB, but took up 1.5T when converted to Numpy matrices. Why? The arrays were converted to standard int arrays (32-bit x 3 channels) when they could've fit into short (8-bit x 3 channels). Each image was 4x as large as it needed to be.

You can certainly use high-level ML tools (like Keras), but it takes a great deal of work to wrangle your data into a usable format, and even more knowledge to debug an ineffective network.

</comment><comment><USER43> I wonder if they are doing this to complete with course.fast.ai

<comment><USER44> IMO teaching people ML is good in general for Google. 1. it spreads the use of tensorflow, 2. it increases not only tensorflow, but also Google's mindshare, 3. it trains people that may become future Google employees, and/or serves as a useful resource for existing employees

<comment><USER45> Also 4) will increment the usage of TPU's on Google Cloud Platform and subsequently the revenue of their cloud offerings.

</comment></comment></comment><comment><USER46> The Google AI courses are also good for old seasoned ML practitioners who want to learn more about more recent deep learning techniques

</comment><comment><USER47> Having done this course 6 months ago, this is a fantastic introduction to the major concerns of practical machine learning.

</comment><comment><USER48> Can't view. Requires a Google account.

<comment><USER49> Have you tried to create one?

</comment></comment><comment><USER50> How does this compare to the CS229 lectures by Andrew Ng? (the recorded lectures, not the MOOC)

<comment><USER51> CS229 @ Stanford is very math/proof intensive. This might be somewhat similar to CS221 or the Coursera course.

</comment></comment><comment><USER52> serving videos from youtube without alternatives doesn't make it accessible. proxying for identified videos esspecially videos from this crash course would be useful.

<comment><USER53> >serving videos from youtube without alternatives doesn't make it accessible.

Why is that?

</comment></comment><comment><USER54> this site is so broken on firefox mobile they should be embarrassed.

</comment><comment><USER55> In the course, in lecture

"Reducing Loss: Gradient Descent"

is

"Convex problems have only one minimum; that is, only one place where the slope is exactly 0. That minimum is where the loss function converges."

The first sentence is flatly wrong: E.g., for positive integer n and the set of real numbers R, function f: R^n --> R where for all x in R^n f(x) = 0, f is convex, concave, and linear, and for all x in R^n x is a minimum and a maximum of f.

Can there be uncountably infinitely many alternative minima for the Google ML problems? Yes, e.g., just enter one of the independent variables twice.

The second sentence is nonsense.

Grotesque, outrageous incompetence!!!!

It has long been known that minimizing a convex function, even a differentiable convex function, with just gradient descent can be just horribly inefficient. A LOT is known about how to do much better than just gradient descent. E.g., there is Newton iteration (right, that Newton, hundreds of years ago) and quasi-Newton. And there's more.

Why so inefficient? Well, draw a picture like Google did except use just two independent variables instead of just the one in the Google picture. Then see that the resulting, convex "bowl" can be like a long, narrow boat with a very gentle slope in one direction and a very steep slope in an orthogonal direction. Yes the cross section of the bowl can be, first cut, an ellipse with one short axis and one long one. Sure, the axes are eigenvectors, etc. and the ellipse is part of a local quadratic approximation. Well, gradient descent keeps going back and forth nearly parallel to the short axis of the ellipse and making nearly no progress on the long axis. People have known this and known good things to do about it for, uh, at least half a century.

For the Google ML problems, might (1) tweak Newton iteration to improve the rate of convergence to a minimum or (2) at each iteration don't get a gradient descent but get a supporting hyperplane of the epigraph of the convex function, as the iterations proceed, accumulate these hyperplanes, notice that they lead to an approximation of the full epigraph, and use linear programming or some tweak of that to minimize the hyperplane approximation to the convex function -- there is much more that can be said here. By the way, the convex function for that ML problem is quite special, e.g., quadratic.

A lot has long been known and well polished in regression and classification, e.g., with TeX markup:

N.\ R.\ Draper and H.\ Smith, {\it Applied Regression Analysis,\/} John Wiley and Sons, New York, 1968.\ \

Leo Breiman, Jerome H.\ Friedman, Richard A.\ Olshen, Charles J.\ Stone, {\it Classification and Regression Trees,\/} ISBN 0-534-98054-6, Wadsworth \& Brooks/Cole, Pacific Grove, California, 1984.\ \

C.\ Radhakrishna Rao, {\it Linear Statistical Inference and Its Applications:\ \ Second Edition,\/} ISBN 0-471-70823-2, John Wiley and Sons, New York, 1967.\ \

A good start on convexity is

Wendell H.\ Fleming, {\it Functions of Several Variables,\/} Addison-Wesley, Reading, Massachusetts, 1965.\ \

Total, fun, dessert ice cream on convexity is Jensen's inequality; right away can use it to prove a lot of classic inequalities.

Gee, look on the upside!!!! From this sample, the claims of machine learning (ML) revolutionizing the economy are nonsense!!!! And for startups, don't much have to worry about serious competition from Google!!!!

<comment><USER56> You should recheck your definitions on convexity.

>function f: R^n --> R where for all x in R^n f(x) = 0

This hyperplane is not convex. A convex curve by definition can not be equal to its tangent at any point.

Edit: I should specify, I mean a convex curve cannot be completely equal to any of its tangents, obviously it will equal each tangent at a single point.

<comment><USER57> It sounds like you are describing curves that are strictly convex. Curves that are convex, but not strictly convex, can intersect their tangents at more than one point, or even at every point.

I'm going by the definition of convex function given in Rudin's "Principles of Mathematical Analysis", Apostol's "Calculus", Wikipedia, and MathWorld.

<comment><USER56> Fair enough. I suppose pointing out that the authors merely omitted "strictly" wouldn't have served GP's point as well.

<comment><USER55> Strictly convex need have no role in this Google ML material. Just convex is enough.

</comment></comment></comment></comment></comment><comment><USER58> Is there something like this for Java programmers?

<comment><USER59> ML concepts are independent of programming language. I suggest not to let language preferences stand in the way of listening to lectures and understanding the subject.

For implementing exercises using Java, you have a bunch of good options:

1) The most direct equivalent to Pandas+Tensorflow I can think of is DL4J. They have a good comprehensive set of concept and implementation tutorials [1].

2) TF APIs have a Java port and can be used from java desktop and console applications [2]. So a second but slightly more difficult option is using TF Java port + Spark APIs.

[1]: [https://deeplearning4j.org/documentation](https://deeplearning4j.org/documentation)

[2]: [https://www.tensorflow.org/install/install_java](https://www.tensorflow.org/install/install_java)

</comment></comment><comment><USER60> Which languages and libraries does it use? Is it based in Google libs, like Tensor Flow?

<comment><USER61> Clicking through to the homepage, the full title is: "Machine Learning Crash Course with TensorFlow APIs".

</comment></comment><comment><USER22> They do have pre-reqs training material with reference links. [https://developers.google.com/machine-learning/crash- course/...](https://developers.google.com/machine-learning/crash- course/prereqs-and-prework#prerequisites)</comment>


Spacex Launch Webcast: Formosat-5 Mission - <AUTHOR> http://www.spacex.com/webcast <comment><USER2> Spacex launched a satellite to the space and returned back to earth safely faster than our JenkinsCI builds most of our apps.

<comment><USER3> I'd blame your source code more than Jenkins. Our jenkins instance runs our CI in a couple of minutes.

<comment><USER4> Not everyone is working on projects that are little more than CRUD webapps. I've worked on code that took hours to compile even on beefy machines.

<comment><USER5> I've worked on code that took that long to compile, and the cause of the problem was usually bad recursive makefiles doing work over and over again.

At the other extreme I've also worked at Google and have seen insanely complex C++ code bases compile in 30 seconds flat.

Multiple hour build times usually show that nobody smart enough had the interest and authority to solve the problem properly. It doesn't generally mean that your project is awesome. It certainly isn't something that I would brag about.

<comment><USER4> OK, that's great for google, but "complex" doesn't necessarily mean much. Do you think the linux kernel devs are slouches? The gcc devs? Both of those projects take hours to compile on reasonable hardware, and that's not for lack of trying.

Also, sometimes it's not about code, it's about digital assets, which is going to be IO bound. I've worked on projects that took a full working day to do a clean compile and others where it was necessary to do binary integrations instead of code because the overall build time would otherwise be measured in days or weeks.

</comment></comment></comment></comment></comment><comment><AUTHOR> Successful landing on Please read the instructions. 15 consecutive landings

<comment><USER6> And as always, the live video feed of the landing was clunky :). They should nail one with a steady stream.

<comment><USER7> Every time I've watched a landing, the video feed is janky. One might speculate that they don't want to live-stream an embarrassing failure but that seems a bit odd when it would be more embarrassing to have an explosion during launch, losing the payload.

Edit: vvvv that sounds quite reasonable, you'd think they'd do something like throw a buoy 100m away to house the actual satellite link and just do wifi from the barge to the buoy.

<comment><USER8> No need for speculation - the feed never cuts out on the return-to-launch-site landings. It's only wonky on the ship landings.

Bouys bounce around a lot - the drone ships have special station-keeping thrusters to stay in place.

<comment><USER9> The rocket engines also put out a ton of ionized particles that can interfere with the satellite signal.

</comment></comment></comment></comment></comment><comment><USER10> Boring.

.

Which is why it's beautiful, despite the less than brilliant connection :)

Like passenger planes leaving airports, I'll never not find this amazing.

</comment><comment><USER11> They were supposed to attempt to land/rescue some other part of the rocket this time. There was nothing about it in the webcast.

<comment><USER12> That'd be the fairing, the 'nosecone' that covers the payload during launch. It wasn't shown in the webcast because it has a low probability of success (they've made several attempts so far with varying degrees of accuracy and damage) and because there's no live feed from the fairing as it descends.

The aim is to get it to land in a predetermined spot in the ocean with steerable parafoils, then eventually to stick a 'bouncy castle' at that spot to soften the landing.

<comment><USER13> Didn't one of the mars rovers bring its own bouncy castle? Hollywood has since used that in several movies.

<comment><USER12> Yep — Pathfinder and the Mars Exploration Rovers (Spirit & Opportunity) both used airbags. NASA has since switched to the 'sky crane' method for Curiosity and the fortcoming Mars 2020 rover, lowering higher-mass payloads to the ground without contaminating the surface with rocket exhaust (as with Phoenix and the Vikings).

</comment></comment></comment></comment><comment><USER14> 12th launch of this year. 50% increase from their prior best year.

They can make it to 20.

</comment><comment><USER15> Still mindblowing after 15 successful landings

</comment><comment><USER16> This is just so amazing. Just looks so effortless. Completely magical what they have accomplished in so little time. Thank you, Elon, for being so incredibly brave and persistent in your mission(s).

</comment><comment><USER17> Did it sound to anyone else like they were shooting from inside the SpaceX cafeteria?

<comment><USER18> They do. The partial second floor overlooking mission control is the SpaceX cafeteria.

</comment></comment><comment><USER19> I've seen each and every one of these and it never tires.

<comment><USER20> Same here. Still watch recorded in cases I missed live webcast although much less exciting. I hope ride up there within the next 20 years, hopefully sooner.

</comment></comment><comment><USER21> When is their low orbit ISP coming?

</comment><comment><USER22> Stuck the landing right in the center.. the accuracy is just amazing

<comment><USER23> I'm more impressed they're able to do it with a rocket that can't hover.</comment></comment>


No more nerds: how Dungeons and Dragons finally became cool - <AUTHOR> https://www.theguardian.com/games/2019/jul/13/no-more-nerds-how-dungeons-dragons-became-cool-stranger-things-game-of-thrones <comment><USER2> I dunno, the people in that picture don't exactly look... cool. I love D&D but have had a hard time finding a group of people who play who I would actually want to be around. Maybe that's a little harsh though..</comment>


An Analysis of the Costs of Storing Data on the Ethereum  Blockchain (2018) [pdf] - <AUTHOR> http://busfactor1.ca/kruhft/eth0000-data-storage-cost-analysis--2018.pdf <comment><AUTHOR> Any questions? Eratta? Comments?

<comment><USER2> Nice contrast, i'm sure there's many applications not using logs when they should be.

Perhaps a brief explanation of gas in ethereum for the the uninitiated and rough USD cost at todays median gas settings from [https://ethgasstation.info](https://ethgasstation.info) or similar.

If cryptokitties 2.0 comes along you may be waiting a long time to store this data without seriously increasing costs. Depending on the use case that might be a hard choice to live with.

<comment><AUTHOR> You might find the other papers interesting:

[http://kruhft.busfactor1.ca/the-eth-papers/](http://kruhft.busfactor1.ca/the- eth-papers/)</comment></comment></comment>


Ask HN: Do SMBs still care about their websites? - <AUTHOR> Do you see in your local market a movement of B2C SMBs shutting down their websites, leaving their web presence to Facebook/Instagram/Twitter only?<p>Edit: added "B2C" context to question. <comment><USER2> Context: I own a CrossFit gym with my wife which happens to be the largest (both in physical size and active membership) in an ~80-100 mile radius. I also have a full-time job developing applications for enterprise and government clients and I used to be a full-time freelancer focusing on custom websites and custom WordPress plugins.

Our website is invaluable. We have all our ad landing pages there so it's a constant source of warm local leads. I think it's grown in importance since we tightened the integration with Facebook and Instagram. We don't really do much search/display advertising, it's all social proof, boosted posts, ads to get people to our events, "people whose friends have liked our page," etc.

If the website's importance is 10/10 I'd put Facebook at an 8 and Instagram at a 5. I'd take simple website + solid FB ad strategy over flashy website + leaky bucket ad strategy 100% of the time.

</comment><comment><USER3> I've seen this. Google's changes to search over the years have made it increasingly difficult for an SMB website to show up in organic searches. Google seems to be biased towards big national brands in a way that they weren't in the past. That, of course, led to more bidding for AdWords, which drove the pricing up there.

So, SMB owners then try to find some other way to get a presence, many of which make it difficult to use your own website. Facebook, for example, is setup in a way where your FB biz page is highlighted, but the url to your actual site is obscured and buried.

Aside from your examples of Facebook, Instagram, and Twitter...I've also seen SMB's move their ecommerce sales to Etsy, eBay, Amazon, and the like.

<comment><USER4> I read an interesting post just some days ago about Ads in Local Business. The site is horrible, but the content is good:

[http://neilpatel.com/2016/12/15/we-analyzed-120000-google- ad...](http://neilpatel.com/2016/12/15/we-analyzed-120000-google-ads-and- discovered-how-local-businesses-can-crush-their-competition-on-a-small- budget/)

<comment><USER5> > That site is horrible

That's an understatement. I wonder how can a site, that is so designed _not_ to make you read anything, say anything of value.

<comment><USER6> I wondered if it could really be as bad as you were saying... And it sure is, but not in the way that I expected!

</comment></comment></comment></comment><comment><USER7> Not in my case, our website is VERY important.

I opened an escape room business in Idaho about 3 months ago, and we have a website, facebook page, google (+,mybusiness,etc), twitter, and instagram account.

Our website is so important because it's how we connect people to our booking system, and for ranking in Google and we link our Adwords account to it as well.

Then we send people to our website first and on our business cards, promo materials, signs, and more, because it has our FAQ, email contact form, photos, the format we want, Book Now link, the message we want at the top, details about our services, and more.

I couldn't imagine just trying to manage all this with just a Facebook page or Google stuff. People would be very confused.

If I said, just got to our Facebook page, you'd be surprised at how many people say they aren't on Facebook. If I say go to our Google+ page they don't even know what I'm talking about. People call all the time to book because they say "I don't do the Internet thing" (not kidding)

Asking people for reviews is easiest through Facebook. I don't even know how to tell people to review me on Google. Not many folks here appear to be on Yelp it seems. But having a "Review us on..." section of our website will be nice when I add it, and then again, I can just send people to the site.

Lastly, the website is where I can put company specific information like our downloadable flyer, rules and regulations, orientation info, faq, specific details about each of our rooms, some player stats, and official info about our promotions.

So yes, a website for a small business is extremely important in my opinion.

That being said, I have heard of people just going to a Facebook Page, but I believe these are people who don't see the value in connecting EVERY aspect of the web to maximize their online exposure, and don't see the benefits they could reap if they use all these tools together effectively.

<comment><USER8> I had no idea "escape rooms" were a thing. Here's the Wikipedia page, for anyone else who is perplexed: [https://en.wikipedia.org/wiki/Escape_room](https://en.wikipedia.org/wiki/Escape_room)

<comment><USER9> Yeap, and they're spreading fast. Lisbon alone has about a dozen now.

<comment><USER10> And while this is getting more and more OT from the original article... if you enjoy puzzles, it's definitely worth it to go and try it. Me, my partner, and two friends (also a couple) had a really great time doing one. There was a 1hr time limit, and we solved it with about 2 minutes to spare.

I suspect that how much fun it is is going to be pretty specific to how well the room is put together; ours was great. The puzzles were tuned so that the difficulty progressed nicely; a couple quick wins, and then they got harder. It was a mix of searching (find the hidden key) and thinking (from these clues, figure out the combination for this lock). This results in a great mix of individual effort and collaborative problem solving.

Anyway, I'm rambling. Great fun with the right group of people and well-done puzzles.

</comment></comment></comment></comment><comment><USER11> I've seen and have encouraged small business to move their web presence to Facebook and Google Local Business.

We've built a solid amount of websites for small businesses, and in most cases they don't care about maintenance. So in the end they spent a solid amount on a website and hosting, don't change anything for four years and build a new site. Having gained nothing in between.

Having a Facebook page doesn't cost you anything, and on the plus side, if you maintain it you will get free exposure by appearing alongside organic posts on users' timelines. You'll have to work for your likes, but it's your responsibiliy to provide fresh and interesting content. The more users engage with your posts, the more visibility you will get as well.

Twitter seems to be mainly for support and quick updates when you have service problems and Instagram is nice if you have something more in the graphics or design sector. In my country it's also mostly used by geeks and doesn't work if you want to reach a general crowd.

However, as a customer it all depends on what I'm looking for. Want a new tap for the kitchen sink? I'm not going to look for that on Facebook, you'd best have a website with some examples and pricing. If I want to go to a restaurant I only care about their contact information and the menu. If I can find that on Facebook or the Google sidebar, I'm happy.

<comment><USER12> For your kitchen sink example, as a customer, I'm far more likely to go to the local eBay/Amazon equivalent (here it's Mercado Libre, an eBay subsidiary), than to google the web (of course in the U.S. you might find more relevant pages).

</comment></comment><comment><USER13> For most non tech SMBs a website was a necessary evil, like all advertising. Then they get caught up in an SEO scam or two and talking of promotion or online advertising is like suggesting tax fraud.

Google could have dreamed up an Adwords Lite for SMBs and stop defaulting to broad match. If done well it could have made Adwords worthwhile for those who can't just hire an adwords consultant and spend 4 figures. I know several SMBs who would never touch Adwords again. The two I know who use adwords well both have someone pretty technical around. One of those does more on Etsy than their site.

As @tyingq says Google's updates have mostly promoted brands and large businesses. Now add the freshness updates and they're hurting all the mainly static small sites. Facebook did much the same when they hugely cut the views pages can achieve. FB went from hugely helpful to almost a waste of time for many.

It's little wonder so many aren't seeing the point.

<comment><USER14> Google does have a much simplified version of AdWords tailored to small/local businesses. I'm not sure how keywords work in there as I've only seen it a handful of times and don't remember.

I'd think broad match modifier works how someone new to search ads would expect normal broad match to work.

</comment></comment><comment><AUTHOR> I do. It is happening everywhere in Brazil. Small companies see no reason in keeping a stand alone website with very low visit figures whereas their fanpages, profiles in massive social networks leverage engaged visitors. I can see many companies not renewing their domain names registrations simply because it does not matter to have a domain name. Custom branded email address? Pointless, since all comms are made inside the social network.

<comment><USER15> Here in Ecuador I see the same thing. Part of the reason is that carriers compete by offering free social network access even for plans which don't have any data (there is no net-neutrality here).

So if you are targeting consumers, a big chunk of those consumers cannot even access your website at all, but they can find you on facebook.

<comment><AUTHOR> Yes, that's one step farther into the darkness. We are not there yet in Brazil. You still need to pay for data, even if it's for 100MB a month, which would give you access to the open web. But, of course, the preference would be to browse Facebook and likes with no limits.

</comment></comment></comment><comment><USER16> Only a few samples, but:

\- Restaurants seem to keep them up. They need to post menus, and links to OpenTable and other reservation sites.

\- K-12 Schools keep their own sites

\- Other small businesses (martial arts schools, dry cleaners, etc) seem to be leaving their old sites on standby. Big drop in forum activity.

<comment><USER17> All I want from restaurant websites is a grid of pictures with prices, and a reservation button with hours of operation prominently displayed. I don't care about anything else, but the food, price, and when I can go. Amazingly this is hidden under nonsense mission statements, and other fluff no one cares about except the chefs.

<comment><USER18> What is even weirder is that many restaurants actually don't contain that information at all. I have been to many places that did not have opening times or wrong (old) opening times. Weird.

</comment></comment></comment><comment><USER19> I have seen this happen a few times. Sometimes businesses switch to turnkey/template sites, but if all they need is an address in Google Maps it isn't necessary a lot of the time.

Conversely, as a web developer, I have stopped working with this segment completely. Non-tech organizations of 1-2 people are way more hassle to deal with than larger organizations.

<comment><USER20> > Conversely, as a web developer, I have stopped working with this segment > completely. Non-tech organizations of 1-2 people are way more hassle to deal > with than larger organizations. reply

I'm seriously considering doing the same. What keeps me working with the 'small guys' is that on some level it's more enjoyable to me. I like making people happy, and when things go right these small clients are very happy, and they care, whereas no single individual in the typical company I work for really gives a shit in isolation.

With the small clients, I get to advise them about every step of the project, and they care about the result because it affects them directly in some way. As a contractor for a bigger company, however, easily half the decisions come from up the hierarchy and lead to work that I truly consider unnecessary. Usually this doesn't matter and they don't mind paying and nobody ultimately really cares about any of it. It's just work.

With the small clients I hate 1) often working fixed-price, 2) haggling over tiny budgets and taking risks, 3) incessant micro-managing in my area of expertise, 4) managing the personal psychological make-up of the client because there is no organization to temper it (or being a manager, I guess). But these same things mean that what I do is _cared_ about, and money I receive is _worth_ something more than just a higher number on my bank account...

Of course, as far as 'just work' goes I'm pretty happy with my situation. The actual work is still challenging and the output can still be somewhat rewarding. But in the back of my head there's this constant little voice asking me if this 'convenience' is actually worth energy and creativity (at least programming-wise) that I end up losing just to pay for my lifestyle.

I mean, if I have so many options as a programmer, would it not be silly to 'just be thankful for how good things are right now' when I'm one part of a small group of people who can actually choose to do other things that make me happier?

Any suggestions for a next step or alternative?

(this is from the perspective of a web developer, although I've reached a point where I'm looking into other programming fields)

<comment> eric-hu I get that it's more gratifying when things go well. Personally, I've dealt with enough assholes in the tiny end of SMBs to never want to go into that space again.

If you're wondering what else is out there, perhaps the work isn't that challenging to you. You have a lot of freedom right now though. Why not explore other forms of programming in your free time and see what piques your interest?

<comment><USER20> Yeah, I'm trying to do that. I guess I was just wondering if anyone had good suggestions, both when considering future work and considering the 'fun' side of it.

For now I've been having fun doing more serious app-development, I'm looking into native (mobile) development via React Native, and I'm eyeing chat bots in the context of psychology/health (CBT, lifestyle, etc.).

So I'm okay for now with not moving beyond the UI/app-ey side of things, but part of me is curious for more intellectually challenging stuff that requires a bit more 'computer science' (although I never studied that).

<comment> eric-hu I'm in that discovery phase myself. I've found that some things which sounded interesting were interesting sounding achievements, but not actually interesting for me to do. For some kinds of open source projects, I completed some work then my interest died; I spent some time in the midst of my 2nd or 3rd issue not getting to the work and feeling bad about it.

So my suggestion is to just try different things out and see what you enjoy. Don't be afraid to put a project down and tell yourself it's okay.

</comment></comment></comment></comment></comment><comment><USER21> In my, completely unscientific but likely not that all inaccurate, observation SMBs are pushing hard on Facebook. Twitter is dead and people don't waste their money adversiting there. Google is still there but it's become such a mess that the difficultly of getting high rankings combined with the much less precise targeting (compared to say Facebook) has made it a lot less attractive recently.

</comment><comment><USER22> Sorry, it would be nice if someone could explain what "SMB" means. To me, it's a network file sharing protocol, and a basic Google search confirms this as the top definition.

<comment><USER23> [https://en.wikipedia.org/wiki/Small_and_medium- sized_enterpr...](https://en.wikipedia.org/wiki/Small_and_medium- sized_enterprises)

</comment></comment><comment><USER24> I know a small company that shut down it's Facebook page while keeping it's website (an old one, from 2005). Probably because it's not selling directly to it's end-customers, so the web site is focused on contact info (for store chains etc) + static web pages with product information. B2B doesn't seem to use Facebook very much at all.

<comment><AUTHOR> Yes, I agree with your point. I have edited the question to add the B2C context around SMBs.

</comment></comment><comment><USER25> It probably depends if you use your website to generate income.

I currently work for a small business that sells dedicated lines, MPLS etc. They invest heavily in their website and use it to generate leads for the sales team.

Myself, on the other hand, I switched from a Wordpress powered blog to a simple, html "About me" website, mainly because I didn't use it and didn't want to keep up with the maintenance.

I can understand if smaller companies, that need nothing more than a business card on the web, use Facebook or some other hosted service for their presence.

</comment><comment><USER26> I used to work for an online booking company that supplied its SMBs with a business profile page, Facebook widget, website widget and reputation management. Almost universally, they all felt that their website was very important and spent an inordinate amount of money on it...some well into the 6-figures, which was much more than they spent on our service. However, also almost universally, they'd get almost no bookings through the website widgets. Customers overwhelmingly preferred dealing with them through Facebook and our business profile page. Also, our business profile pages showed up much higher in Google for non-name searches.

I'm sure there are some SMBs that really do have a need for a website and it really is important to them, but for the verticals that we dealt with, there was a huge perception gap between the perceived and actual value of the website and a profound misunderstanding of the preferences of their customers. The question of whether SMBs still care about their websites is, to me, less interesting than the question of whether SMBs _should_ care about their websites.

</comment><comment><USER12> During an "Entrepreneurship" class, some classmates started investigating about SMBs web presence.. and yes, a LOT of SMBs find their Facebook page to be a lot more valuable than their website, often forgoing the website.

It's been a lot worse for them since Facebook started asking for money in order to promote posts, but the ease of use and features of Facebook coupled with an audience beat a website hands down.

Companies included home cooking / office meals, small design and deco items sales, nail polish and beauty supplies, even a general hardware store.

I do think a website is important, but a Facebook page even more so.

Another huge channel here is WhatsApp.

</comment><comment><USER27> Yup I've witnessed the same migraion... food stalls like The Poutinerie are facebook / twitter only.

I would've expected a website 5 years ago, but they seem fine.

Google "The Poutinerie" and their 'website' comes up top billing ( London based peep )

</comment><comment><USER28> I had actually spent a lot of time researching this particular use case - creating and maintaining website as B2C SMB and their way of promoting themselves to customers. I came to the conclusion that if you are mortar-and- brick business, focused on local people, the current web is actually totally broken for you and the only feasible solution for ads are location based ad systems.

In this particular case I distinguished between businesses which are location based and businesses which are able to sell their goods and services on the Internet. For those who are able to sell on Internet, there is plenty of options for e-commerce solution etc, but for local businesses it doesn't make economically sense to maintain website.

During my research I talked with 50 local small-business owners in SF and each one of them had website. They all agreed that it is luxurious expense and half of them had problem to maintain the site and only 10/50 were able to update their website at least once a month. What was kind of shocking for me to find out was that around 60% of these people was paying $50+/month for shitty website and almost nobody was willing to spend more money on their website.

When asking about social media(Facebook, Instagram, Snapchat, Twitter, WhatsApp) - 95% had profiles there and were actually having them more updated than their business websites. Unfortunately, majority of the owners were pretty skeptic about the advertisement there and never tried, for example, targeted FB ads. Most of them never heard about this option.

Here is couple of findings:

\- When asking, why do they have a website, some of them replied that it is must have in eyes of the customer - you don't exist if you don't have website, right?

\- When asking what's their monthly number of visitors on their websites, 60% didn't know.

\- When asking about the most used communication channel, almost everyone said phone. One barber was actually running WhatsApp group and worked pretty well for him.

\- When asking how much they will be investing into a website - the majority said that they will not increase their costs for web and ads.

From my point of view, web is actually total nonsense for local businesses, it's unnecessary expense and the owners could do much better if they orient on social media and local advertisement. The real problem for them is that they are not willing to maintain 10 social network accounts and constantly update them, because they need to run their real business as well.

One funny thing I found out was that 10 out of 50 said that old-fashioned promotional flyer into mailbox still works best for them and has the biggest response and they will spent more money on that. Kinda surprise for me in SF in the beginning of 2016.

<comment><USER29> It's interesting that they're spending $50/mo on a shitty website. I wonder if they'd be happy with a static site with their location, hours, and contact info (something to prove to millennials that they exist) for google to scrape, with links to their social media profiles. This costs less than $2/mo on S3 after the setup.

</comment></comment><comment><USER30> curious about things like thumbtack and homeadvisor for small service businesses.

I've heard of business using those as alternatives to their own websites. To me I'd much rather own my own website site, but I think I'm in the minority.</comment>


Show HN: VilniusNow – see Vilnius through the eyes of the people - <AUTHOR> http://vilniusnow.lt

<comment><AUTHOR> Just launched a site dedicated to showcasing pictures of my city - Vilnius, Lithuania.

Any feedback would be appreciated

</comment><comment><USER2> I liked it.

<comment><AUTHOR> Thank you!</comment></comment>


[Pragmatic Bookshelf] 40% Off Black Friday Celebration One-Day Sale - <AUTHOR> http://media.pragprog.com/newsletters/2010-11-22.html

<comment><USER2> It's hard to get excited about a promotion that brings down the price to about what Amazon already sells their books. I'm not trying to diss the PragProgs. I like their books as they're very useful to me. I do own a couple. But from the customer's perspective, it's not a big deal.

Now the screencasts is another matter. 40% off is a pretty good deal.

<comment><USER3> The sale is exciting to those of us who want to buy the ebooks, which are not available from Amazon. (I'm thinking about now buying _Seven Languages in Seven Weeks: A Pragmatic Guide to Learning Programming Languages_ on Friday.)

<comment><USER2> Good point, I had glossed over ebooks. Many thanks!

</comment></comment></comment><comment><AUTHOR> _"We're offering 40% off everything in the store—books, PDFs, and screencasts. There are a couple of exceptions, including The Pragmatic Programmer book itself (ironically, we don't publish it), and the Programming Ruby 1.9 which is already on its anniversary sale."_

<comment><USER4> Also note:

      the coupon is valid from 00:01PST to 23:59PST on Friday,
      November 26th. (PST is 8 hours behind GMT)

That is, it's almost Saturday for me in GMT+9.

And interestingly it's starting from 00:01, not from 00:00.

</comment></comment><comment><USER5> Their servers must be getting hammered now. Even pragprog.com is a bit slow.

It's going to be interesting on Black Friday if this is how responsive it is just for the announcement.

</comment><comment><USER6> Great, I've already bought 3 PragProg books this month alone. Don't know how I can resist the temptation from buying more. PragProg is probably my favorite publisher, I really enjoy their books.

</comment><comment><USER7> Does anyone have a "best of" list or a list of their favorite books from Pragmatic Bookshelf?

<comment><USER8> To be honest, their entire catalog belongs in my "best of" pile.

</comment></comment><comment><USER9> That link didn't work for me, this did though [linked removed] thanks<AUTHOR>
<comment><AUTHOR> This link is about the 2009 edition of the promotion, now it's a different coupon code and it works only for friday 26 of november.

</comment></comment><comment><USER10> Can you get a discount on the beta books?

<comment><USER11> No, that's one of the exceptions called out.

<comment><USER10> It says "Beta on paper", but I meant to ask about Beta eBooks, sorry. The HTML5+CSS3 one looks interesting, for example.</comment></comment></comment>


Ask YC: Ideas for FluidDB? - <AUTHOR>

FluidDB is a centralized but scalable datastore.  In it, the meaning of a given object is derived from the tags placed upon it-- and any user can place namespaced tags on any object.  The idea is that ad-hoc standards for object IDs and tag names will emerge through use.  I think this is a pretty novel shared-data concept and have had a couple ideas for it, but I'm sure news.yc will have additional novel applications. (I am a fan of FluidDB and its creator, but am otherwise unaffiliated.)
What interesting apps can you imagine with this shared-data system?
You can get a high-level idea of it here: http://abouttag.blogspot.com/2009/08/guardian-1000-novels-everyone-must-read.html
You can get into the details here: http://doc.fluidinfo.com/fluidDB/ <comment><USER2> I guess I'll get the ball rolling. We're building (and close to releasing) an app that's focused on Twitter users. It has FluidDB objects that each correspond to a Twitter user. Because the FluidDB objects aren't owned, other apps can put their tags onto the objects too. The query language lets you search on objects based on any tags (that you have read permission for), so we're hoping we'll soon see a wide variety of tags on these objects, which will give us a bunch of new ways to identify users on Twitter, to tag them arbitrarily, etc. The are obvious things like ratings, or people you've met, but there are also lists (a tagged set of users is just a list, more or less) including queries that cross lists, and things like <http://tunkrank.com> scores about Twitter users. FluidDB gives a place to put all that stuff - without requiring you to ask permission or for your needs to be anticipated - and it gives you a way to query on it too.

That's just a first app. We'll open source it. _I_ can think of many more, as you might hope :-)

Terry Jones (disclaimer: I work for Fluidinfo, makers of FluidDB)

</comment><comment><USER3> Mobile carriers store subscriber information, and this causes all sorts of problems with mobile device characteristics, roaming and other special attributes that only the carriers control.

It would be great if each subscriber could own their own profile and have it synched with their own mobile device. This would require carriers to inter- operate with FluidDB, but since many of them use LDAP, it wouldn't take much to have this type of interface made available via a FluidDB client library.

This is framework of a simple application that I've been kicking around.

</comment><comment><USER4> I plan to build a visual FluidDB query editor with DSLs on top. One may call it "language workbench". Here is <http://fluidmix.org/> the site. Will be doing the query tree interface next. I believe FluidDB is a great platform for search and I intend to take advantage of it.

<comment><USER2> Hi Orlin

I hadn't been to fluidmix since you first mentioned it. the site looks really nice.

</comment></comment><comment><AUTHOR> So, the lack of response is a bit surprising. Am I wrong that FluidDB is novel? Or are people just having a hard time with the idea _because_ it is novel?

<comment><USER2> Hi Jeremy

The information model takes some time to sink in - at least for most people (including me). Examples will help people to "get" it better. We have one coming out soon (next couple of weeks). I can give you an early look if you're ineterested.</comment></comment>


Building a CPU with Haskell, part 1 - <AUTHOR> http://yager.io/CPU/CPU1.html <comment><USER2> This is awesome. I have to figure out how to use it with stack instead of Haskell Platform! ;)</comment>


Are PhD Students Irrational? - <AUTHOR> https://lareviewofbooks.org/article/phd-students-irrational/#! <comment><USER2> If you enjoyed this piece, you should read "Planet Loser":

"If you actually want to know why people persist in going to grad school, or trying to be screenwriters or musicians or professional athletes or actors — or, if we're getting really real about it, journalists and writers, the kind that write for The Atlantic — it's because our culture insists relentlessly that certain kinds of work have intellectual and aesthetic value, that they are a way to be a Somebody, and also that you never, ever give up on your dreams. There is no message that we deliver to children and adolescents more relentlessly than that they should pursue their dreams with manic focus and unflagging persistence, no matter how hard and often they fail, and that they will be assured of eventual victory. Never, never, never give up. Whether you think you can, or you think you can't, you're right! If you believe it, you can achieve it. Go to a middle school sometime. Look at the posters on the wall. It's like a one-party state, propaganda of the most ubiquitous and intense variety. When I was a long-term sub at my local junior high I was amazed; it was like some sort of totalitarian reeducation camp, with the purpose being to indoctrinate in all who passed through the doors that failure stems always and only from a lack of nerve."

[http://fredrikdeboer.com/2016/04/24/planet- loser/](http://fredrikdeboer.com/2016/04/24/planet-loser/)

<comment><USER3> So do you think that a 20 year old deterministically absorbs the values of their culture? Don't be so paternalistic. College students are not children; they are not your students.

The value of a PhD is not high because the life of a PhD is desirable. Academia provides a (flawed) society with a permanence and thoughtfulness that competes with non academic america. Leisure and camaraderie is everywhere. It provides a sense of identity which is hard to find, so supply is high and price is low.

Within the cult there are bad actors who misrepresent their value, and selfishly collect rewards. Nothings perfect.

<comment><USER4> Not just 20 year olds but everyone is affected by, and can't help but absorb a great deal of, the values of their culture. That's what it means to call it their culture.

</comment></comment></comment><comment><USER5> In my experience people don't enter into PhDs expecting to make a lot of money. And STEM PhDs get paid, so it isn't as if we accumulate debt. Often times a PhD is the only route to doing what you love. It isn't possible to do academic research in industry. Personally when I entered into my PhD I really liked that the research I was doing had a noble purpose.

On the other hand, a PhD is way too long. And if you stop at year 4 or so you have basically wasted 4 years. I did not appreciate how much my values would change over time and sort of got locked into trying to finish my PhD. Trying to finish a PhD when you don't have passion for the subject anymore is very stressful. This experience is disturbingly common.

Finally, the author early on implies that the job market for STEM PhDs is not good. At least in physics, this is only true in academia. I know many people who have transitioned successfully to data science, or who have gotten a post doc. Overall, the unemployment rate of physics PhDs is low.

<comment><USER6> There was a time when a failed postdoc was in a very depressing financial situation. Corporations didn't want to hire a PhD, because they were overqualified and likely had some anti-authoritarian streak that led them to attempt academia.

STEM PhDs are more in demand these days, but some still don't know it and need encouragement (and sometimes a little help) transitioning to industry.

<comment><USER5> I agree with your sentiment, and think that postdocs are the real problem with academia currently. Besides the low pay, being a graduate student is not a bad job - you get good job security, interesting problems, good mission, and can somewhat choose your boss. A postdoc is a terrible, soul-crushing, low-pay job. At least for the physics postdocs I have seen - I have heard that in the humanities a postdoc is actually a highly sought after position.

<comment><USER6> One of the saddest conversations I've ever had was with a two-time assistant professor who had just been denied tenure (second chance is the last).

A mutual friend had referred him to me for advice on how to transition to industry. I gave him a pep-talk and explained how to build a portfolio to show skills that matter in the workplace. I think he turned things around, but for a little while he couldn't stop thinking about the "wasted" years. PhD, postdoc(s), 3-5 years of assistantship, repeated once, ... it's a tragedy for such a smart, capable person to succeed so much and yet feel like they failed.

</comment></comment></comment></comment><comment><USER7> When talking only about software and CS, I think there is one more way to look at this issue (and has been mentioned multiple times in this comment thread).

Let us say the typical software engineer's day is filled up with the following types of tasks:

1\. What patio11 describes, although in a different context: "Don't try to make a career out of optimizing the SQL queries to display a preference page on a line of business app at a company that no one has ever heard of." [1]

2\. Some kind of algorithmic work (e.g. writing a compiler)

3\. Big data, machine learning etc. (consuming the results of algorithms, hence different from 2)

4\. Software architecting

My view is that after a while, most people want to move from group 1 to one of the other groups. This gives a good way to explain the pursuit of Ph.D. even when it is not economically rational (both in time and money cost) - it is a pursuit of something which is not mundane as long as you make the effort.

A teacher of mine said: "You are going to be spending about 40 years in your career. If you take 5 out of that to do a Ph.D. you are not going to reflect on it with regret. And at the end of it you have a Ph.D. too."

When you combine this with the possibility that you will be mostly hanging out with elastic minded students as PG once put it and you might actually have the time of your (intellectual) life, the decision doesn't look all that irrational.

[1] [https://training.kalzumeus.com/newsletters/archive/do-not- en...](https://training.kalzumeus.com/newsletters/archive/do-not-end-the-week- with-nothing)

</comment><comment><USER8> The article doesn't really touch on this, but for people embarking on a PhD program, school is what they've been doing 8 or so hours a day for 16+ years and school is what they're good at (or else they wouldn't have gotten into the PhD program). It's the least disruptive path post-college for a certain group of college grads and their choice is to trade potential financial gain for stability and familiarity. That may not be the best choice for each one of them, but it's certainly not irrational.

<comment><USER9> That rather fails to account for people who "go back to school" for a PhD after finishing their undergrad and working for a while.

<comment><USER10> Those who can, Do...

<comment><USER11> ...those who can't, comment on HN

</comment></comment></comment></comment><comment><USER12> I'd say it entirely depends on the assumption that to get a PhD means that you want to be a professor (and teach in the US at a reasonably decent school and get tenure one day). If that really is your goal, then yes, you should think very hard before you do it.

The Adjunct scam is really, truly atrocious. Adjucts and grad students can unionize now, though, and it only takes one strike in the e. coli labs for a research university to cave...

However, PhDs do have valuable (and financially rewarding) roles in nonprofit research institutions that are not subject to the same challenges as universities (though I sure hope you like writing grants),

In "real" industry positions, a PhD can be a way to bulwark yourself against "the engineer trap", where you get promoted into a project management role and become unable to actually do anything except munge Excel documents. It is a gamble.

I have an engineering background, so I can't really speak to what it is like to, e.g., have a PhD in English. But, depending on your degree, there are also really cool gigs at places like microsoft research -- they do everything from cell biology to audio, now, as well as employ "pure theory" CS folks.

<comment><USER13> >The Adjunct scam is really, truly atrocious. Adjucts and grad students can unionize now, though, and it only takes one strike in the e. coli labs for a research university to cave...

Grad students have been forming unions since the '70s, and it hasn't had any impact. There are so few tenure track positions you can't afford to even _slightly_ irritate the people you need to get one, and everyone views their stint as a TA or an adjunct as temporary.

</comment></comment><comment><USER14> I don't see why being irrational is bad. A "labour of love" is precisely something that does not obey the law of diminishing returns. Passion and interest are much more sustaining and fulfilling than a job in the finance sector just because it is highly paid.

I think "rationality" is a stupid assumption, often wrong. Of course, people need more money, but a lot of people are also willing to make sacrifices for something else - spouses, children, parents, religion or country. Money is important, but not the overriding concern for everyone. I often feels that the correct word for a "rational" individual in the sense of economics is "sociopath".

Ralph Nader once said about his organization, that "you can bring your conscience to work" every day. This actually counts for a lot.

However the basic point of the article, that the Universities are conning students is perfectly valid (source: ex-PhD student, can relate to the frustration). This point needs to be better made, avoiding slinging mud on the intellect of the hapless students. This article is an example of how you can write a dubious article, even in the presence of good data.

<comment><USER15> Everyone on this thread seems to be focusing entirely on _money_ when determining whether or not something is _rational_. The Economics 101 definition of _rational_ that I recall from university is only tangentially related to strictly monetary value -- things usually provide non-monetary value as well ("opportunity cost", etc.). If every trade were always based solely on monetary value, trade would be a zero-sum game, but it's not: fair trade is a win-win situation. Seller wins because the product is worth more to the buyer than it cost to produce; buyer wins because the product is worth more _to them_ than it cost to buy. If this weren't the case, one or both parties would decline the trade (assuming, of course, as economists are wont to do, that both parties are rational).

As you point out, there are plenty of non-monetary benefits to academia: passion, interest, fulfillment, pride, etc. These things have different value to different people depending on their own interests and goals. If the total benefit (tangible _and_ intangible) is worth more to someone than the opportunity cost, then, according to economics, that person is _still acting rationally_ in such pursuits.

I'm not an economist, but this is seriously undergraduate-level Introductory Economics material.

</comment></comment><comment><USER16> Hard to say. The RAND institute did a study concluding that there is no meaningful shortage of STEM graduate degrees, that the aversion to these degrees, to the extent it exists, is a rational response to completion times, attrition rates, job prospects, and salaries when compared to other professional degree programs such as MBA, law, or medicine. However, this wasn't quite the same as concluding that it is _irrational_ to pursue these degrees, just that we should stop scratching our heads and wondering why more people don't pursue them.

Payscale has an interesting ranking of graduate degrees by program, which is more useful than raking all holders of a particular graduate degree together.

[http://www.payscale.com/college-salary- report/grad?page=49](http://www.payscale.com/college-salary- report/grad?page=49)

Unfortunately, it doesn't break out MS and PhD holders by subject studied. So while you do get to see a specific ranking for an MBA or JD holder from UCLA or MIT, you only get overall salary info for PhDs from MIT, not PhD in Computer Science from MIT vs Electrical Engineering from Berkeley. That would be far more useful.

As it stands, a PhD doesn't show up until spot 29 on this list, but then again, a PhD in CS might, so hard to say.

To me, the attrition rate from PhD programs is an under appreciated aspect of this discussion. A lot of people from elite Law, MBA, and MD programs are floored when they hear the attrition rate from PhD programs. Seriously, the attrition rate form an elite law or med school tends to be well below one half of one percent. Attrition rates form elite engineering and science PhD programs range from 35%-50%.

In any case, I'm always glad to see this discussed. The only people who seem to think there is a "shortage" or STEM graduate students are people who have a financial interest in hiring STEM graduate students. Almost every other analysis concludes that people with the freedom to choose their career in the US (free of visa restrictions that limit their career choices) are largely acting rationally by pursuing other graduate degree programs (or no graduate degree program).

<comment><USER17> > ... the attrition rate form an elite law or med school tends to be well > below one half of one percent. Attrition rates form elite engineering and > science PhD programs range from 35%-50%.

Many PhD programs offer full tuition, health care, and a yearly stipend with teaching duties. OTOH, there may be less incentive to give someone the boot if they're bringing in large sums of money by paying their way through, which is how most master's and professional degrees work (as I understand).

<comment><USER9> >Many PhD programs offer full tuition, health care, and a yearly stipend with teaching duties.

Do we have data showing that entry-level jobs (the first three to five years) in STEM industries have 35-50% attrition rates?

<comment><USER16> I'd guess the attrition rate is higher than that. Just a guess though. Would also be interesting to see if the attrition rate is mainly transferring to a different field rather vs. changing jobs within the same field.

</comment></comment></comment></comment><comment><USER18> A PhD is a great way to immigrate and this drives down PhD stipends.

A PhD is a great way to continue learning.. And good for the overall economy because companies rely on math, science and engineering to produce products.

<comment><USER19> A PhD at a good institution is also a great way to open doors which would otherwise remain closed. I found that opening emails with "I'm a DPhil student at the Oxford University Computing Lab and" resulted in a dramatic increase in how helpful people would be.

<comment><USER20> That's probably the Oxford part doing the trick, not the DPhil part ;-) I am only half-joking.

<comment><USER19> I _did_ say "at a good institution". ;-)

</comment></comment></comment></comment><comment><USER21> Since we are on HN, how does this irrationally differ from the hordes of people going into start ups hoping they will be the next AirBnB?

I would be interested in comparing the chances of a graduate student becoming a tenured professor versus a fresh grad starting a start up that becomes a unicorn.

<comment><USER22> If we stick to PhDs as opposed to the excellent but undervalued MS degree:

Far better chance of becoming a tenured professor (I'd say roughly 1 in 10-20 in my field, geoscience). Plus, like has been said elsewhere in this thread, after 4-7+ years of being underpaid and overworked, you've got a PhD.

However, a freshly-tenured professor at a major research school might make 100k, and might make 150k by full professor. Very few outside of business/medicine/maybe some engineering make more than 200k by full professor.

If you found a unicorn, you probably do a bit better than that, financially.

<comment><USER21> Those seem to be pretty good odds. Thanks for the rough estimate.

You can also hit 'unicorns' as a scientist, for example a major discovery (nobel prize) or innovation (millennium technology prize), which should be taken into account somehow if we want to compare the two.

In start ups there are lots of discussions and analysis of long tails and capturing unicorns (implying lots of failures along the way) but analysis of careers in science never gets the same treatment, although I find many connections between the two (many small failures but big wins). It is always statistics of what an average scientist would achieve rather than what a top scientist would achieve. It would be nice to apply some of the same analyses we do on careers in start ups to careers on science.

</comment></comment></comment><comment><USER23> In computer science, I feel that this definition of irrational only applies in the same way that doing anything that isn't maximizing my $/hour rate is irrational.

Sure, the academic job market is very competitive (though CS arguably has more opportunities than other fields), and landing a solid industry research position is no cakewalk either. But the difficulties in finding a research job don't preclude doing software engineering work to pay the bills.

It's not uncommon to decide a few years into a graduate program that you'd rather just be making more money and drop out for a more lucrative industry/industry research position. For CS PhD students, the job market only seems terrible if you have a very narrow definition of a job (which seems irrational).

<comment><USER24> Yes, CS is way better than most majors in that you can move without too much difficulty from post-graduate studies to a good job. For many other majors, when moving out of acedemia, people end up working at Starbucks, or as a receptionist at a doctor's office. And to top it off, you've got thirty thousand dollars in debt on top of a low paying job.

</comment></comment><comment><USER25> From my reading it seems like the crux of the argument is this:

> We've presupposed a scenario in which there really is a massive oversupply > of PhDs, and thus PhD students must be irrational for treading into an > oversupplied labor market. But that's simply not true. PhD "oversupply" is > just a euphemistic way of talking about the fact that colleges and > universities haven't met student-generated demand with a commensurate supply > of full-time, tenure-track faculty.

PhDs aren't irrational for wasting 5 years on something that won't net them a job... universities are shirking their responsibility to provide a job to those who demand them.

<comment><USER26> Normally I'd agree. Especially if we're assuming purely selfish pursuits ("I want to fuck around in academia forever and get paid, supply/demand be damned!").

However, if I see it through the lense of someone who desperately wants to move the world forward -- and at any personal cost -- then I start to entertain the thought that, maybe, it's unfortunate that that some can't find jobs, and they're deserving of my sympathy.

Simillarly, I work on a bunch of OSS. A lot of it is based in academia and unlikely to become mainstream. I'm also unlikely to benefit directly from it, and I don't really enjoy the process of creation within the field of software. However, I feel that the stuff I'm working on -- even with the slim odds of mass adoption -- are worth dumping my time into because it'll move the industry forward _if_ it's successful. You can bet that I'd love to get paid to work on this stuff, but I also understand that's unlikely to ever happen. And sure, I could let other people work on these projects so I can instead go off and have fun, but I believe that my background fills a particular niche that would likely be left open if I didn't sacrifice my time and energy in these pursuits.

Maybe it's the same with these PhD peeps. Each person believes that they're essential to progress, and their principles prohibit them from abandoning their self sacrifice in the pursuit thereof. I think I can understand the frustration; perhaps someone really ought to give these selfless people some money so, you know, they can make all of our lives better.

(In other words, I think "passionate (if not masochistic) altruism" might be mistaken as "entitlement" here.)

<comment><USER27> > if I see it through the lense of someone who desperately wants to move the > world forward and at any personal cost then I start to entertain the thought > that, maybe, it's unfortunate that that some can't find jobs, and they're > deserving of my sympathy

Maybe it would work if PHDs benefited from some kind of basic income, as long as they dedicate their life to research or teaching. Tenure should not be the only way to achieve the basic necessities for researchers. In the long run society benefits from all the fundamental research being done, which would be difficult to sponsor in the private domain, where immediate profits are most valued.

</comment></comment></comment><comment><USER28> Should universities not be held accountable for the fraud that they have perpetrated on their students? The tobacco companies were taken to court because their products had long-term consequences which they persistently minimized, while continuing to push product; it seems universities have done the same. If anything, the universities have better, clearer data than the tobacco companies, and the universities have far more people with clear understandings of both statistics and the observable consequences of getting a PhD. Throwing away the last years of your life because of cancer doesn't seem clearly worse than throwing away the best years of your life seeking a PhD.

<comment><USER9> You should read the article more closely. It advocates _exactly_ holding universities accountable, and in particular, holding universities accountable for deliberately casualizing academic (ie: PhD-holding) labor by converting tenured professorships into adjunct positions, even while expanding enrollments and using the tuition money to hire more and more administrators.

It would be more accurate to say that universities are exploitative than that the existence of a self-reproducing class of professional scientists is irrational.

>But perhaps the best way to get answers is to talk to the parents of college students, the ones footing the bill. Ask whether they'd rather pay that ballooning college tuition for lazy rivers on campus, for fruit baskets and housekeepers for executive staff, and for other rising non-classroom costs; or for more top PhD talent, smaller classrooms, more one-on-one attention from faculty, more independent studies and research opportunities with faculty, and a stable faculty who won't have to leave halfway through their student's college career. Then you'll get a sense of the real, unmet demand for PhDs, and the irrationality of a market that isn't delivering.

<comment><USER28> I am making the case for lawsuits with billion dollar settlements or judgements against universities, as well as ongoing concessions. The article advocates what I would describe as 'half-measures', not actual penalties. Many have proposed criminal charges against tobacco company executives, and I see no reason that the university heads should be held to a lower standard.

<comment><USER9> How can I contribute ;-)?

</comment></comment></comment></comment><comment><USER29> No. Reason: [https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headline...](https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headlines)

<comment><USER29> Hah, downvotes. I thought people would get that I was 'trying' to joke.</comment></comment>


Nobody Knows What Running Looks Like - <AUTHOR> http://www.theatlantic.com/health/archive/2014/10/nobody-knows-what-running-looks-like/381171/

<comment><USER2> [https://hn.algolia.com/?q=Nobody+Knows+What+Running+Looks+Li...](https://hn.algolia.com/?q=Nobody+Knows+What+Running+Looks+Like#!/story/forever/0/Nobody%20Knows%20What%20Running%20Looks%20Like)</comment>


Parse compatible API - <AUTHOR> http://nimbleparse.com <comment><USER2> You keep using that word, API. I do not think it means what you think it means.</comment>


Challenger Engineer Who Warned of Shuttle Disaster Dies - <AUTHOR> http://www.npr.org/sections/thetwo-way/2016/03/21/470870426/challenger-engineer-who-warned-of-shuttle-disaster-dies <comment><AUTHOR> Recently on HN, Petition to honor Ebeling.

[https://news.ycombinator.com/item?id=11180905](https://news.ycombinator.com/item?id=11180905)

</comment><comment><USER2> Related:

[http://www.sltrib.com/home/3690697-155/utah-bird-savior- and-...](http://www.sltrib.com/home/3690697-155/utah-bird-savior-and-rocket- scientist)</comment>


Common Lisp vs. Scheme macros - <AUTHOR> http://eli.thegreenplace.net/2007/09/16/common-lisp-vs-scheme-macros/ <comment><USER2> Oh well, I was hoping for side-by-side code comparisons.

<comment><USER3> Side-by-side comparisons wouldn't help much. Either it would be an example where you want hygiene, and the Scheme version would look way better, or it would be an example where you don't want hygiene, and the CL version would look way better.

Not having hygiene can be convenient, e.g. if you want to introduce a binding that will be used by the calling code, and don't want the calling code to have to specify the variable name every time, e.g. the aif macro.

<comment><USER2> Still, for someone who knows lisp macros and would like to learn scheme macros (err...me) the side-by-side would have been helpful.</comment></comment></comment>


Sr. Recruiter - <AUTHOR> I would like to post jobs for our company Headspring. How may I do so?<p>Thank you, <comment><USER2> Please read the Guidelines and FAQ links at the bottom of the page. They'll tell you that job ads are only allowed in the monthly "Who is hiring?" threads, which are posted on the first business day of each month. As an example, here is this month's thread:

[https://news.ycombinator.com/item?id=16282819](https://news.ycombinator.com/item?id=16282819)

If you have further questions, you can contact the moderators at firstname.lastname@example.com.

<comment><AUTHOR> Thank you so much. :)

</comment></comment><comment><USER3> there are threads by [https://news.ycombinator.com/submitted?id=whoishiring](https://news.ycombinator.com/submitted?id=whoishiring) where job postings can be added

<comment><AUTHOR> Thank you :)</comment></comment>


LOLcommits - Takes a snapshot with your webcam every time you git commit code - <AUTHOR> https://github.com/mroth/lolcommits

<comment><USER2> Brilliant. There should be a webpage of these.</comment>


Top 5 Bitcoin Price Prediction - <AUTHOR> https://coingape.com/top-5-bitcoin-price-prediction-2018/ <comment><USER2> This isn't really helpful content so much as quotes speculating how high Bitcoin will go. An honest assessment would at least include _" $0 by 2025."_

And that prediction comes from me, as I just made it up. I don't hold it close to my heart, but it's certainly possible.</comment>